Projected population exposure to heatwaves in Xinjiang Uygur autonomous region, China

The intensification of heatwaves dues to climate change is a significant concern, with substantial impacts on ecosystems and human health, particularly in developing countries. This study utilizes NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and projected population data accounting for China’s population policies to project changes in various grades of heatwaves (light, moderate, and severe) and the population exposure to heatwaves (PEH) in Xinjiang under three shared socioeconomic pathways (SSP1–2.6, SSP2-4.5, and SSP5-8.5). The results show that the number of days and intensity of heatwaves in Xinjiang are projected to increase. Heatwaves occurring in Xinjiang will predominantly be severe heatwaves (SHW) in the long-term under the SSP5-8.5 scenario, and the number of SHW days projected to increase by 62 ± 18.4 days compared to the reference period. Changes in heatwaves are anticipated to influence PEH, estimating population exposure to light, moderate, and severe heatwaves (LPEH, MPEH, and SPEH) at 534.6 ± 64 million, 496.2 ± 43.5 million, and 1602.4 ± 562.5 million person-days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of PEH is projected to be consistent with that of the reference period, with high values persisting in Urumqi, Kashgar and Hotan. Changes in PEH are primarily driven by climate effects, followed by interactive effects, while population effects contribute the least. Therefore, mitigating climate change is crucial to reduce the PEH in Xinjiang.


Dataset
The observational daily maximum temperature and relative humidity in this study are obtained from the gridded daily scale dataset of CN05.1, which are provided by National Climate Center, China Meteorological Administration.The dataset is constructed by anomaly approach method based on 2416 meteorological stations in China, with the spatial resolution of 0.25° × 0.25°.The dataset has been quality controlled and widely applied as the reference to evaluate and calibrate model simulations 41 .
The climate models dataset are derived from the latest version of the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP-CMIP6, https:// www.nccs.nasa.gov/ servi ces/ data-colle ctions/ land-based-produ cts/ nex-gddp-cmip6).The dataset is based on output from the CMIP6, using downscaling and bias correction/spatial disaggregation method to obtain high-resolution daily gridded meteorological dataset with the resolution of 0.25° × 0.25°4 2 .The dataset has been used extensively in regional studies of extreme weather events 43,44 .In this study, we use 16 climate models to project heatwaves in Xinjiang (Table 1).The multi-model ensemble (MME) approach can effectively reduce the uncertainty of the simulations 45 .Therefore, in order to improve the accuracy and reliability of projection, the MME of 16 climate models is used in this study.
The population data from 2021 to 2100 are taken from the Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100 (https:// doi.org/ 10. 6084/ m9.figsh are.c.46057 13).The dataset is produced taking into account the population policies implemented in China in recent years (fertility promoting policies and population ceiling restrictions of megacities), and is the high-resolution gridded data (1 km × 1 km) that better matches the actual situation in China 46 .The population datasets are upscaled to the precision of 0.25° by summation method to match with the climate data.

Definition of heatwaves
Considering the impact of heatwaves on human health, the heatwave index (HI), which combines air temperature and relative humidity, is utilized in this study as an indicator for identifying heatwaves 47 .The HI can be calculated as follows: In which TI denotes the torridity index of the current day, TI′ is the critical value of torridity index, TI i rep- resents the torridity index of the i-th day before the current day, nd i is the number of days from the i-th day to the current day, and N is the duration of hot weather process (days).
The TI can be obtained as follows: (1)  where T max is the daily maximum temperature (℃), RH represents the daily relative humidity (%).The critical value of torridity ( TI′ ) is used to determine if the weather is hot.If TI exceeds TI′ , it indicates that the day is hot weather.TI′ is calculated using the quantile method with the following formulas: where Q i p represents the i-th quantile value, p is the quantile (0.5 in this study), n is the length of TI series, j is the j-th TI , X denotes the sample sequence of the TI in ascending order.
According to the magnitude of the HI, Heatwaves are graded into light, moderate, and severe heatwaves (LHW, MHW, and SHW).The classification standard is shown in Table 2.

Metrics of model performance
The interannual variability skill score (IVS) 48 is used to evaluate the interannual variability of the simulations compared to the observations, which is expressed as: where STD o and STD m are the interannual standard deviations of the observation and simulations, respectively.
To evaluate the performance of the model in reproducing the spatial pattern of heatwaves, the Distance between Indices of Simulation and Observation (DISO) 49

Weighting methodology
MME is used with no rules for determining the number of models to be used, and variations in model weighting schemes are used in different studies 50 .Based on the overall performance of the model simulations, to give the highest weight to the best performing models, this study uses performance weighting to generate MME.The weights then have the value: (2) Table 2. Classification of heatwaves.

Population exposure to heatwaves
Population exposure to heatwaves (PEH) is defined as the number of people exposed to heatwaves, is generally calculated by multiplying the population in each grid cell by the number of heatwave days 14 .Therefore, the unit of PEH is person-days.This study focuses on PEH for three periods including near-term (2021-2040), midterm (2041-2060), and long-term (2081-2100) under three SSP scenarios (SSP1-2.6,SSP2-4.5, and SSP5-8.5).
According to the definition of PEH, changes in PEH are affected by climate effect, population effect and interactive effect.To evaluate the impact of these effects on future PEH changes, we calculate the relative contribution of each effect according to the approach of Jones et al. 14 .The relative contribution of each effect is calculated as follows: where P r is the population from the base-year of 2000 (person), H r indicate the annual days of heatwave (days) in the reference period (1995-2014), H and P are the change in the number of heatwave days and population in the future period compared to the reference period.CR cli , CR pop , and CR int represent the contribution rates of changes in the climate, population, and their interactions, respectively.

Changes in historical heatwaves and population exposure
The spatial distribution of heatwave days for different grades during the reference period is presented in Fig. 2a-c.The observational results show that the spatial distribution of heatwaves is closely associated with the topographic features, mainly distributed in basin areas.LHW exhibit the broadest spatial extent and the greatest number of days (Figs.2a).The number of LHW days ranges from about 0-23 days, with only some regions of Turpan and the Kumtag desert having more than 19 days.MHW affect smaller geographical area compared to LHW, with the number of heatwave days varying from 0 to 18 days (Figs.2b).Regions with high values of MHW continue to be predominantly located in Turpan and the Kumtag Desert.As heatwave severity increase, the number of heatwave days decreases, and the affected area shrinks further.The affect area of SHW is reduced, particularly in the Junggar Basin (Figs. 2c).The number of SHW days is distributed between 0 and 14 days, and the area with more than 12 days is only distributed in Turpan.In summary, the heatwaves in Xinjiang during the reference period are dominated by LHW and MHW, and the high value regions of different grades of heatwaves are located in Turpan, Hami, and the Kumtag Desert.
The combination of different grades of heatwave days and population produces the PEH for the reference period (Fig. 2d-f).The results show that the population exposure to light, moderate, and severe heatwave (LPEH, MPEH, and SPEH) amount to 85.1 million, 42.1 million, and 20.5 million person-days, respectively.Notably, the spatial pattern of heatwave and PEH are significantly differs due to the effect of the spatial distribution of population.High PEH values are not in regions with frequent heatwaves, but mainly in densely populated regions such as Urumqi, Kashgar and Hotan.

Model performance evaluation
Before projecting the possible future changes of heatwaves in Xinjiang, the simulation capability of 16 climate models is evaluated by comparing the number of simulated heatwave days with observations during the reference period.As shown in Fig. 3a, the regions in Xinjiang with the highest number of heatwave days are located in the Tuha Basin and Kumutag Desert, and the mountainous regions have never experienced heatwaves.The spatial pattern of the model simulation is close to that of the observations.However, compared with observations, the results of the model simulations are overestimated or underestimated in some regions.To assess the reliability of models, we quantify the ability of individual models to reproduce the interannual variability and spatial patterns of heatwaves in Xinjiang using IVS and DISO, respectively.
As can be seen in Fig. 4, the simulation ability of the models varies in different aspects, with models excelling in simulating interannual variability not necessarily performing better in capturing spatial patterns.Compared to observations, GFDL-ESM4 and MIROC6 are the optimal models for simulating interannual variability and spatial patterns, respectively.In order to assess the comprehensive performance of individual models, a composite (12)

Projected changes in heatwave
In comparison to the reference period, the number of different grades of heatwave days is anticipated to increase in the future under three SSP scenarios (Fig. 6).The count of LHW days continues to increase under the SSP1-2.6 and SSP2-4.5 scenarios, with the substantial increase of 7.8 ± 1.1 days (MME ± one standard deviation) in the long-term under the SSP2-4.5 scenario (Fig. 6a).Both MHW (Fig. 6b) and SHW (Fig. 6c) days exhibit consistent increase under each scenario.The largest increase is in the long-term under the SSP5-8.5 scenario, with an increase of 9.8 ± 1.7 days and 62 ± 18.4 days, respectively.It is worth noting that in the long-term under the SSP5-8.5 scenario, heatwaves in Xinjiang may no longer be dominated by LHW and MHW, but by SHW.This suggests that Xinjiang is projected to experience more frequent and intense heatwaves.
The spatial distribution of the variations in heatwave days is crucial for gaining the deeper understanding of the future changes of heatwaves in Xinjiang.Here, the model agreement is denoted by the number of models that have the same sign for changes in heatwave days with the MME results.Compared to the reference period, the number of LHW days increased in most regions of Xinjiang (Fig. 7).Under future scenarios, the greater increases in the number of LHW days are mainly located at the margins of the Tarim Basin and in the northern part of the Junggar Basin, where fewer LHW days occurred during the reference period.Notably, in the longterm under the SSP5-8.5 scenario, there is a substantial decrease in the number of LHW days, predominantly located in the Tuha Basin and the Tarim Basin.In general, areas with large variations in the number of LHW days exhibit higher model agreement.
The number of MHW days is projected to increase in most of Xinjiang under future scenarios compared to the reference period, except for decrease in MHW days in parts of the Tuha Basin in the long-term under the SSP5-8.5 scenario (Fig. S1).Different from LHW and MHW, the number of days of SHW will increase in almost all regions (Fig. S2).Not only is the increase in the number of SHW days significant, but the regions experiencing the greatest increase are primarily located in the regions with the highest number of heatwaves during the reference period.Regions with large increases in the number of MHW and SHW days have high model agreement.In summary, in comparison to the reference period, the area of heatwaves in Xinjiang is expanding, the number of heatwave days is increasing, and the severity of heatwaves is intensifying under different SSP scenarios.

Estimation of population exposure to future heatwaves
To evaluate the population exposed to heatwaves under different SSP scenarios, we calculate PEH for different grades by combining the number of heatwave days with the projected population.Illustrated in Fig. 8, under the SSP1-2.6 scenario, the maximum of LPEH (Fig. 8a), MPEH (Fig. 8b), and SPEH (Fig. 8c) occurs in the mid-term, reaching 388.5 ± 62.9 million (MME ± one standard deviation), 236.1 ± 50.9 million, and 205.2 ± 57.1 million person-days, respectively.Under the SSP2-4.5 and SSP5-8.5 scenarios, the different grades of PEH continued to increase over time.Furthermore, SPEH exceeded the sum of LPEH and MPEH in the long-term under the SSP5-8.5 scenario, at 1602.4 ± 562.5 million person-days.
The spatial patterns of the projected PEH resemble those of the reference period, indicating significant spatial divergence.Regions with high value of LPEH during the future period align with those in the reference period, primarily located in Urumqi, Kashgar and Hotan (Fig. 9).In addition, regions that experienced no LPEH during the reference period are projected to exhibit LPEH in the future, such as Kizilsu and the southern parts of Yili.It is important to note that while the total LPEH in the projection period is greater than that during the reference period, not all regions.LPEH decreases in most regions of Xinjiang, and the regions that increase are mainly located in regions with larger LPEH during the reference period, with high model agreement for this change (Fig. 10).The regions with LPEH decreases are more extensive in the long-term under the SSP1-2.6 and SSP5-8.5 scenarios.Correspondingly, the population in most regions of Xinjiang reduces in comparison to the reference period in the long-term under the SSP1-2.6 and SSP5-8.5 scenarios (Fig. 11).The spatial patterns of projected MPEH (Fig. S3) and SPEH (Fig. S4) are similar to those of the projected LPEH, with nearly identical distributions in both high and low value regions.Compared to the reference period, not only the range of MPEH (Fig. S5) and SPEH (Fig. S6) will expand, but exposure will increase in most regions.In summary, it is projected that more people will be affected by the serious heatwave.The increase in PEH will pose serious threat to future ecosystems and social development.In order to mitigate this threat, it is crucial to understand the effects of changes in PEH.

Relative contributions of climate and population changes
Changes in PEH are influenced by climate, population and their interactions.To investigate the relative importance of each factor, we assessed the change in PEH and the relative contribution of the factors for each future period compared to the reference period under different SSP scenarios (Fig. 12 and Table 4).Compared to the reference period, the largest increase in LPEH occurred in the long-term under the SSP2-4.5 scenario, amounting to 528.8 ± 64.4 million person-days.The factor contributing significantly to the increase is the interactive effect, with a contribution of approximately 42.8%, followed by the climate effect, with approximately 35.8%, and lastly, the population effect, with approximately 21.4%.MPEH and SPEH increase the largest in the long-term under  The major factor contributing to this change is the climate effect, with contribution of about 53.1% and 64.7%, respectively.In summary, the primary driver of PEH changes in Xinjiang are climate effects, followed by interactive effects, with population effects contributing the least.

Conclusion and discussion
Xinjiang is an important part of the global arid zone, experiences frequent heatwaves.The frequency of heatwaves in Xinjiang has significantly increased with climate change, posing the serious threat to human health 51 .Therefore, projecting the impact of heatwaves on human health in Xinjiang is the crucial and pressing task.In considering the effects of heatwaves on human health, it is essential to not only focus on the heatwaves but also population dynamic.Currently, most population data used in projecting future population exposure studies do not account for changes in China's population policy, potentially leading to biased projections 52 .To enhance the accuracy of PEH projections, we utilize the dataset that incorporates recent changes in China's populationrelated policies, including population ceiling restrictions in megacities and fertility promotion, to project PEH in Xinjiang.Therefore, this study aims to provide the first comprehensive evaluation of the variation in PEH in Xinjiang under climate change by integrating heatwaves and population changes, and to quantitatively assess the contribution of each factor to changes in PEH.The results of this study provide a scientific basis for mitigating heatwave hazards and formulating sustainable development policies.
The spatial distribution of heatwaves in Xinjiang is closely related to topographic features and is predominantly distributed in basin areas, such as the Junggar Basin, the Tarim Basin and the Tuha Basin.This is primarily due to the geographical characteristics of the basins, which contribute to the occurrence and intensification of heatwaves.For instance, the lower elevation, higher temperatures and humidity of these basins render those regions susceptible to heatwaves.The topography of the basins restricts air circulation, leading to the trapping of heat and further intensification of heatwaves.Additionally, the predominantly desert and semi-desert land cover types in these areas make them more prone to heatwaves.During the reference period, heatwaves in Xinjiang are dominated by LHW and MHW.This is consistent with the results obtained by Liu et al. 53 who examined various grades of heatwaves in China.In terms of area affected, LHW is the largest, followed by MHW, and SHW is the smallest, with the most significantly change in Yili.
The spatial distribution of PEH is significantly different from that of heatwaves.Regions with higher number of heatwave days do not necessarily have higher PEH or may not have it, such as the Kumtag Desert.The spatial distribution of PEH is determined by both heatwaves and population, and the geographical distribution pattern of the Mountain-Desert-Oasis system determines that the population of Xinjiang is mainly concentrated in oases 54 .Therefore, regions with higher PEH values are mainly located in densely populated areas such as Urumqi, Kashgar and Hotan.Since most areas in Xinjiang are susceptible to heatwaves, the spatial distribution of PEH depends mainly on that of population.By evaluating the simulation ability of individual models, it is found that GFDL-ESM4 and MIROC6 are optimal models for capturing interannual variability and spatial patterns of heatwaves in Xinjiang, respectively.However, the highest overall ranking among the ensemble models is CanESM5.Weighting individual model members within the ensemble based on their performance is considered as a way to reduce uncertainty 55 .Considering the differences in model performance, and in order to provide more reasonable projections, we evaluate the simulation capabilities of the MME.The comparison reveals that the overall simulation capability of MME outperforms all the remaining models in the ensemble except CanESM5.Individual model projections are affected by higher internal climate variability making the projections more uncertain than MME 56 .Therefore, in order to provide more reasonable information and uncertainty in the projection, this study relies on MME to project future heatwaves and PEH variations in Xinjiang.
Compared with the reference period, the total number of heatwave days in Xinjiang is projected to increase, aligning with findings from previous studies 37,57 .However, using only air temperature to identify heatwaves in their study ignored the effect of relative humidity on human health.The combination of high temperatures and high relative humidity can influence the heat dissipation capabilities of the human body.Relying on temperature may lead to an underestimation of the impact of the environment on human health 58 .Although the majority of heatwaves in Xinjiang are categorized as dry heatwaves, the consideration of combined air temperature and relative humidity are necessary under the climate become wetter 59,60 .In addition, different from the finding that the increase in heatwaves grows sequentially from SSP1-2.6 to SSP5-8.5, the increase in LHW and MHW in the long-term under the SSP5-8.5 scenario is smaller than that of under the SSP2-4.5 scenario 61 .This discrepancy is primarily attributed to the substantial increase in SHW.Consequently, the heatwaves in Xinjiang are projected to be more serious in the long-term under the SSP5-8.5 scenario.Spatially, the region affected by heatwaves in Xinjiang is projected to expand under different SSP scenarios.Despite the overall expansion of heatwaves, the mountains regions remain unaffected by heatwave in all scenarios.This inconsistent with previous studies projecting heatwaves in China that found heatwaves in the mountains of Xinjiang in the future 36 .The discrepancy arises from the chosen method for identifying heatwaves.Our study calculated the HI for samples with daily maximum temperatures exceeding 33 ℃, effectively excluding colder regions where heatwaves are less likely to occur.Consistent with previous studies projecting an increase in heatwave severity across most regions of Xinjiang 57 , our findings project that most regions of Xinjiang will experience frequent SHW in the long-term under the SSP5-8.5 scenario.Furthermore, regions with decrease in the number of LHW days are characterized by an increase in MHW and SHW.
Climate change and population growth are projected to result in an increase in PEH in Xinjiang.The maximum values of PEH occur in the mid-term under the SSP1-2.6 scenario.This may be due to the SSP1-2.6 scenario represents a sustainable development scenario, which is characterized by slower population growth and lower greenhouse gas emissions 62 .Different grades of PEH continue to increase over time and reach the maximum in the long-term under the SSP2-4.5 and SSP5-8.5 scenarios.Under the SSP2-4.5 scenario, future fertility rate is moderate due to the effect of two-child policy, while the SSP5-8.5 scenario exhibits lower fertility 46 .Thus, the contribution of population effects to changes in PEH is consistently higher under the SSP2-4.5 scenario than SSP5-8.5.Compared with SSP5-8.5, Xinjiang has more population but less PEH under the SSP2-4.5 scenario.The results suggest that changes in PEH in Xinjiang are more sensitive to climate change than to variations in population.In terms of spatial distribution, the spatial pattern of PEH during the projection period mirrors that observed in the reference period.High PEH values are still in the densely populated areas such as Hotan, Kashgar and Urumqi.Although the total PEH during the projection period exceeds that of the reference period, spatially, many regions show a decrease in PEH compared to the reference period.Under the SSP1-2.6 scenario, PEH decreases in many regions, but the number of heatwave days does not reduce in these regions.Thus, the decrease in PEH under the SSP1-2.6 scenario is mainly the result of lower fertility and mortality rates 63 .However, the reduction in population is not only related to fertility and mortality rates, but also to migration.Under the SSP5-8.5 scenario, with social and economic development, population concentrates in cities.Therefore, under the SSP5-8.5 scenario, the total number is increasing, although exposure is projected to decrease in many regions.Changes in PEH are influenced by climate, population and their interactions.Understanding the relative importance of factors influencing changes in PEH is crucial for developing climate change adaptation and mitigation policies in the study area.Since the primary driver of PEH change in Xinjiang is climate effect.Thus, consistent with the findings of Li et al. 64 , climate mitigation is particularly important in order to reduce population exposure to unprecedented heatwaves.

Figure 1 .
Figure 1.Overview of the study area.(a) Topography of Xinjiang.(b) Spatial distribution of population density in 2000.(c) Population in Xinjiang from 2000 to 2100.The maps in figure are created using ArcGIS v10.6 (https:// www.esri.com/).
is used.For the observed values (A = (a 1 , a 2 ,…, a n )) and the model-simulated values (B = (b 1 , b 2 ,…, b n )), the DISO can be calculated as follows: where a and b is the mean of A and B, respectively.The values of IVS and DISO closer to 0 indicate that the model is better model performance.

Figure 5
compares the performance of MME and each model within the ensemble in simulating the spatial patterns and interannual variability of heatwaves.The blue dashed line and the red dotted line represent the IVS

Figure 2 .
Figure 2. Spatial distribution of different grades of heatwave days and population exposure to heatwaves in Xinjiang during the historical period.

Figure 5 .
Figure 5. Scatter diagram of the model performance.

Figure 6 .
Figure 6.Projected changes in the (a) light heatwaves, (b) moderate heatwaves and (c) severe heatwaves from the MME under different SSP scenarios, relative to the reference period (1995-2014).The colored bars are based on MME, and error bars indicate the standard deviations of the multi-model ensemble projections.

Figure 7 .
Figure 7. Spatial distribution of projected relative changes of light heatwaves days for different SSP scenarios compared to the reference period.The dotted areas denote regions where at least 75% of models agree with MME on the sign of the change.

Figure 8 .
Figure 8. Population exposure of (a) light heatwaves, (b) moderate heatwaves and (c) severe heatwaves under different SSP scenarios.The colored bars are based on MME, and error bars indicate the standard deviations of the multi-model ensemble projections.

Figure 9 .
Figure 9. Spatial distribution of population exposure to light heatwaves in future periods under different SSP scenarios.

Figure 10 .
Figure 10.Spatial distribution of projected relative changes in population exposure to light heatwaves for different SSP scenarios compared to the reference period.The dotted areas denote regions where at least 75% of models agree with MME on the sign of the change.

Figure 11 .
Figure 11.Spatial distribution of projected relative population changes for different SSP scenarios compared to the reference period.

Figure 12 .
Figure 12.Decomposition of the changes in population exposure to heatwaves in future period under different SSP scenarios.The colored bars are based on MME, and error bars indicate the standard deviations of the multimodel ensemble projections.
S i is the sum of the rankings of the model's simulated interannual variability and spatial patterns. where

Table 3 .
Weights and ranks of 16 climate models.

Table 4 .
Relative contribution of population, climate, and their interactions to changes in exposure under different SSP scenarios.